Dynamic Traffic Prediction-Based Energy Management of Connected Plug-In Hybrid Electric Vehicles with Long Short-Term State of Charge Planning

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dc.authoridCoskun, Serdar/0000-0002-7080-0340
dc.authoridZhang, Fengqi/0000-0001-9811-2593
dc.authoridLin, Xianke/0000-0001-5695-248X
dc.contributor.authorZhao, Nan
dc.contributor.authorZhang, Fengqi
dc.contributor.authorYang, Yalian
dc.contributor.authorCoskun, Serdar
dc.contributor.authorLin, Xianke
dc.contributor.authorHu, Xiaosong
dc.date.accessioned2025-03-17T12:25:37Z
dc.date.available2025-03-17T12:25:37Z
dc.date.issued2023
dc.departmentTarsus Üniversitesi
dc.description.abstractVehicle electrification, automation, and connectivity in today's transportation require significant efforts in control design to meet conflicting goals of energy efficiency, traffic safety, as well as comfort. The rapid development of intelligent transportation systems (ITS) and the rapid growth of connectivity technologies enable vehicles to receive more information about traffic conditions, which provides a reliable solution for the energy management of plug-in hybrid electric vehicles (PHEVs). This article proposes a predictive energy management strategy (EMS) for connected PHEV based on real-time dynamic traffic prediction. First, the future traffic information is predicted by establishing a wavelet neural network (WNN). Thus, the global driving condition can be predicted. Then, the particle swarm optimization (PSO) algorithm is used to optimize the parameters of WNN to plan a global battery state-of-charge (SOC) reference. Second, a long short-term memory-based velocity predictor is proposed for the predictive EMS, by planning SOC over a prediction horizon based on the global SOC reference. Finally, the performance of the proposed EMS with WNN and PSO-WNN is verified by the actual traffic data. The results show that it can improve the fuel economy by 17.57% and 28.19%, respectively.
dc.description.sponsorshipNational Natural Science Foundation of China [51875054, 51905419, U1864212]; State Key Laboratory Research Project [SKLMT-ZZKT-2022M09]; National Key Research and Development Program Inter governmental International Science and Technology Innovation Cooperation Projects [2021YFE0193800]
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grants 51875054, 51905419, and U1864212, in part by State Key Laboratory Research Project under Project SKLMT-ZZKT-2022M09, and in part by the National Key Research and Development Program Inter governmental International Science and Technology Innovation Cooperation Projects under Grant 2021YFE0193800. The review of this article was coordinated by Mr. Ajeya Gupta.
dc.identifier.doi10.1109/TVT.2022.3229700
dc.identifier.endpage5846
dc.identifier.issn0018-9545
dc.identifier.issn1939-9359
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85149864978
dc.identifier.scopusqualityQ1
dc.identifier.startpage5833
dc.identifier.urihttps://doi.org/10.1109/TVT.2022.3229700
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1787
dc.identifier.volume72
dc.identifier.wosWOS:000991849700024
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Transactions On Vehicular Technology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectEnergy management
dc.subjectState of charge
dc.subjectPlanning
dc.subjectRoads
dc.subjectFuel economy
dc.subjectVehicle dynamics
dc.subjectTrajectory
dc.subjectPlug-in hybrid electric vehicle
dc.subjecttraffic flow
dc.subjectwavelet neural network
dc.subjectparticle swarm optimization
dc.titleDynamic Traffic Prediction-Based Energy Management of Connected Plug-In Hybrid Electric Vehicles with Long Short-Term State of Charge Planning
dc.typeArticle

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